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The Google Professional-Data-Engineer exam comprises multiple-choice and scenario-based questions that test the candidate's knowledge and skills in different areas of data engineering. The test is designed to assess the candidate's ability to design and build data processing systems that meet specific business requirements. Furthermore, the exam evaluates the candidate's proficiency in data analysis, data visualization, and machine learning.

To become a certified Google Professional Data Engineer, candidates must pass the Professional-Data-Engineer Exam. Professional-Data-Engineer exam consists of multiple-choice and scenario-based questions that test the candidate's knowledge of Google Cloud data processing technologies, data storage solutions, and data analysis tools. Professional-Data-Engineer exam also evaluates the candidate's ability to design and implement data pipelines using Google Cloud services, such as BigQuery, Cloud Dataflow, and Cloud Dataproc.

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Google Professional-Data-Engineer Certification Exam is a comprehensive test that covers a wide range of topics related to data engineering. Professional-Data-Engineer exam consists of multiple-choice questions that assess the candidates' knowledge of various Google Cloud Platform services, including BigQuery, Cloud Spanner, Cloud Dataflow, and Cloud Dataproc. Additionally, the exam evaluates the candidates' ability to design data processing systems that are scalable, reliable, and secure.

Google Certified Professional Data Engineer Exam Sample Questions (Q181-Q186):

NEW QUESTION # 181
You work for a farming company. You have one BigQuery table named sensors, which is about
500 MB and contains the list of your 5000 sensors, with columns for id, name, and location. This table is updated every hour. Each sensor generates one metric every 30 seconds along with a timestamp, which you want to store in BigQuery. You want to run an analytical query on the data once a week for monitoring purposes. You also want to minimize costs. What data model should you use?

A. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an INSERT statement every 30 seconds to add new metrics.
B. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, which points to the id column in the sensors table.
3. Use an UPDATE statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.
C. 1. Create a metrics column in the sensors table.
2. Set RECORD type and REPEATED mode for the metrics column.
3. Use an UPDATE statement every 30 seconds to add new metrics.
D. 1. Create a metrics table partitioned by timestamp.
2. Create a sensorId column in the metrics table, that points to the id column in the sensors table.
3. Use an INSERT statement every 30 seconds to append new metrics to the metrics table.
4. Join the two tables, if needed, when running the analytical query.

Answer: D

NEW QUESTION # 182
You are designing a fault-tolerant architecture to store data in a regional BigOuery dataset. You need to ensure that your application is able to recover from a corruption event in your tables that occurred within the past seven days. You want to adopt managed services with the lowest RPO and most cost-effective solution. What should you do?

A. Export the data from BigQuery into a new table that excludes the corrupted data.
B. Create a BigQuery table snapshot on a daily basis.
C. Migrate your data to multi-region BigQuery buckets.
D. Access historical data by using time travel in BigQuery.

Answer: D

Explanation:
Time travel is a feature of BigQuery that allows you to query and recover data from any point within the past seven days. You can use the FOR SYSTEM_TIME AS OF clause in your SQL query to specify the timestamp of the data you want to access. This way, you can restore your tables to a previous state before the corruption event occurred. Time travel is automatically enabled for all datasets and does not incur any additional cost or storage.
Reference:
Data retention with time travel and fail-safe | BigQuery | Google Cloud BigQuery Time Travel: How to access Historical Data? | Easy Steps

NEW QUESTION # 183
Your company is currently setting up data pipelines for their campaign. For all the Google Cloud Pub/Sub
streaming data, one of the important business requirements is to be able to periodically identify the inputs
and their timings during their campaign. Engineers have decided to use windowing and transformation in
Google Cloud Dataflow for this purpose. However, when testing this feature, they find that the Cloud
Dataflow job fails for the all streaming insert. What is the most likely cause of this problem?

A. They have not applied a global windowing function, which causes the job to fail when the pipeline is
created
B. They have not assigned the timestamp, which causes the job to fail
C. They have not set the triggers to accommodate the data coming in late, which causes the job to fail
D. They have not applied a non-global windowing function, which causes the job to fail when the pipeline
is created

Answer: A

NEW QUESTION # 184
You create an important report for your large team in Google Data Studio 360. The report uses Google BigQuery as its data source. You notice that visualizations are not showing data that is less than 1 hour old.
What should you do?

A. Disable caching by editing the report settings.
B. Refresh your browser tab showing the visualizations.
C. Clear your browser history for the past hour then reload the tab showing the virtualizations.
D. Disable caching in BigQuery by editing table details.

Answer: A

Explanation:
Explanation
Reference https://support.google.com/datastudio/answer/7020039?hl=en

NEW QUESTION # 185
Which of these is not a supported method of putting data into a partitioned table?

A. Create a partitioned table and stream new records to it every day.
B. Use ORDER BY to put a table's rows into chronological order and then change the table's type to "Partitioned".
C. Run a query to get the records for a specific day from an existing table and for the destination table, specify a partitioned table ending with the day in the format "$YYYYMMDD".
D. If you have existing data in a separate file for each day, then create a partitioned table and upload each file into the appropriate partition.

Answer: B

Explanation:
You cannot change an existing table into a partitioned table. You must create a partitioned table from scratch. Then you can either stream data into it every day and the data will automatically be put in the right partition, or you can load data into a specific partition by using "$YYYYMMDD" at the end of the table name.

NEW QUESTION # 186
......

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